Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Hou:2021:10.1007/978-3-030-87234-2_28,
author = {Hou, B and Kaissis, G and Summers, RM and Kainz, B},
doi = {10.1007/978-3-030-87234-2_28},
pages = {293--303},
publisher = {Springer},
title = {RATCHET: Medical transformer for chest X-ray diagnosis and reporting},
url = {http://dx.doi.org/10.1007/978-3-030-87234-2_28},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Chest radiographs are one of the most common diagnostic modalities in clinical routine. It can be done cheaply, requires minimal equipment, and the image can be diagnosed by every radiologists. However, the number of chest radiographs obtained on a daily basis can easily overwhelm the available clinical capacities. We propose RATCHET: RAdiological Text Captioning for Human Examined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is trained end-to-end. It is capable of extracting image features from chest radiographs, and generates medically accurate text reports that fit seamlessly into clinical work flows. The model is evaluated for its natural language generation ability using common metrics from NLP literature, as well as its medically accuracy through a surrogate report classification task. The model is available for download at: http://www.github.com/farrell236/RATCHET.
AU  - Hou,B
AU  - Kaissis,G
AU  - Summers,RM
AU  - Kainz,B
DO  - 10.1007/978-3-030-87234-2_28
EP  - 303
PB  - Springer
PY  - 2021///
SN  - 0302-9743
SP  - 293
TI  - RATCHET: Medical transformer for chest X-ray diagnosis and reporting
UR  - http://dx.doi.org/10.1007/978-3-030-87234-2_28
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000712024400028&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/96829
ER  -
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